{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0750], requires_grad=True) tensor([0.9371], requires_grad=True) tensor(14.1322)\n",
      "tensor([1.3249], requires_grad=True) tensor([2.8453], requires_grad=True) tensor(1.9668)\n",
      "tensor([1.8287], requires_grad=True) tensor([3.3151], requires_grad=True) tensor(0.8744)\n",
      "tensor([2.1101], requires_grad=True) tensor([3.3794], requires_grad=True) tensor(0.6958)\n",
      "tensor([2.3192], requires_grad=True) tensor([3.3326], requires_grad=True) tensor(0.6029)\n",
      "tensor([2.4993], requires_grad=True) tensor([3.2584], requires_grad=True) tensor(0.5267)\n",
      "tensor([2.6632], requires_grad=True) tensor([3.1802], requires_grad=True) tensor(0.4606)\n",
      "tensor([2.8153], requires_grad=True) tensor([3.1046], requires_grad=True) tensor(0.4028)\n",
      "tensor([2.9572], requires_grad=True) tensor([3.0333], requires_grad=True) tensor(0.3522)\n",
      "tensor([3.0898], requires_grad=True) tensor([2.9663], requires_grad=True) tensor(0.3080)\n",
      "tensor([3.2137], requires_grad=True) tensor([2.9037], requires_grad=True) tensor(0.2693)\n",
      "tensor([3.3296], requires_grad=True) tensor([2.8450], requires_grad=True) tensor(0.2355)\n",
      "tensor([3.4379], requires_grad=True) tensor([2.7902], requires_grad=True) tensor(0.2060)\n",
      "tensor([3.5393], requires_grad=True) tensor([2.7390], requires_grad=True) tensor(0.1801)\n",
      "tensor([3.6340], requires_grad=True) tensor([2.6910], requires_grad=True) tensor(0.1575)\n",
      "tensor([3.7226], requires_grad=True) tensor([2.6462], requires_grad=True) tensor(0.1377)\n",
      "tensor([3.8055], requires_grad=True) tensor([2.6043], requires_grad=True) tensor(0.1204)\n",
      "tensor([3.8830], requires_grad=True) tensor([2.5651], requires_grad=True) tensor(0.1053)\n",
      "tensor([3.9554], requires_grad=True) tensor([2.5284], requires_grad=True) tensor(0.0921)\n",
      "tensor([4.0232], requires_grad=True) tensor([2.4941], requires_grad=True) tensor(0.0805)\n",
      "tensor([4.0866], requires_grad=True) tensor([2.4621], requires_grad=True) tensor(0.0704)\n",
      "tensor([4.1458], requires_grad=True) tensor([2.4321], requires_grad=True) tensor(0.0616)\n",
      "tensor([4.2012], requires_grad=True) tensor([2.4041], requires_grad=True) tensor(0.0539)\n",
      "tensor([4.2531], requires_grad=True) tensor([2.3779], requires_grad=True) tensor(0.0471)\n",
      "tensor([4.3015], requires_grad=True) tensor([2.3534], requires_grad=True) tensor(0.0412)\n",
      "tensor([4.3468], requires_grad=True) tensor([2.3304], requires_grad=True) tensor(0.0360)\n",
      "tensor([4.3892], requires_grad=True) tensor([2.3090], requires_grad=True) tensor(0.0315)\n",
      "tensor([4.4288], requires_grad=True) tensor([2.2890], requires_grad=True) tensor(0.0275)\n",
      "tensor([4.4659], requires_grad=True) tensor([2.2702], requires_grad=True) tensor(0.0241)\n",
      "tensor([4.5005], requires_grad=True) tensor([2.2527], requires_grad=True) tensor(0.0211)\n",
      "tensor([4.5329], requires_grad=True) tensor([2.2363], requires_grad=True) tensor(0.0184)\n",
      "tensor([4.5632], requires_grad=True) tensor([2.2210], requires_grad=True) tensor(0.0161)\n",
      "tensor([4.5916], requires_grad=True) tensor([2.2066], requires_grad=True) tensor(0.0141)\n",
      "tensor([4.6181], requires_grad=True) tensor([2.1932], requires_grad=True) tensor(0.0123)\n",
      "tensor([4.6428], requires_grad=True) tensor([2.1807], requires_grad=True) tensor(0.0108)\n",
      "tensor([4.6660], requires_grad=True) tensor([2.1690], requires_grad=True) tensor(0.0094)\n",
      "tensor([4.6877], requires_grad=True) tensor([2.1580], requires_grad=True) tensor(0.0082)\n",
      "tensor([4.7079], requires_grad=True) tensor([2.1478], requires_grad=True) tensor(0.0072)\n",
      "tensor([4.7269], requires_grad=True) tensor([2.1382], requires_grad=True) tensor(0.0063)\n",
      "tensor([4.7446], requires_grad=True) tensor([2.1292], requires_grad=True) tensor(0.0055)\n"
     ]
    },
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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 1. 导入数据\n",
    "x = torch.rand([500])\n",
    "y = 5 * x + 2\n",
    "\n",
    "w = torch.rand(1, requires_grad=True)\n",
    "b = torch.rand(1, requires_grad=True)\n",
    "\n",
    "for i in range(2000):\n",
    "    # 2. 进行预测\n",
    "    y_pre = w * x + b\n",
    "\n",
    "    # 3. 计算损失\n",
    "    loss = (y - y_pre).pow(2).mean()\n",
    "\n",
    "    # 4. 反向传播\n",
    "#     if w.grad:\n",
    "#         w.grad.data = w.grad.data.zero_()\n",
    "#     if b.grad:\n",
    "#         b.grad.data = b.grad.data.zero_()\n",
    "    for j in [w, b]:\n",
    "        if j.grad:\n",
    "            j.grad.data = j.grad.data.zero_()\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. 更新参数\n",
    "    w.data -= 0.01 * w.grad.data\n",
    "    b.data -= 0.01 * b.grad.data\n",
    "    \n",
    "    if i % 50 == 0:\n",
    "        print(w, b, loss.data)\n",
    "\n",
    "# 6. 可视化\n",
    "y_predict = w * x + b\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rc('font', size=20)\n",
    "plt.rc('figure', figsize=(20, 8), dpi=100)\n",
    "plt.scatter(x.data.numpy(), y.data.numpy(), c='r')\n",
    "plt.plot(x.data.numpy(), y_predict.data.numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3.9999], requires_grad=True)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.randint(low=1, high=3, size=[2, 2], requires_grad=True, dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 1.],\n",
       "        [2., 2.]], requires_grad=True)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 1.],\n",
       "        [2., 2.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a * a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = b.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.2500, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.2500)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.grad == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 1.],\n",
       "        [2., 2.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0000, 0.5000],\n",
       "        [1.0000, 1.0000]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# c对a中元素的梯度\n",
    "a.grad.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0666, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0666, grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0666)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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